mxnet.np.random.weibull¶
-
weibull
(a, size=None, device=None, out=None)¶ Draw samples from a 1-parameter Weibull distribution with given parameter a via inversion.
- Parameters
a (float or array_like of floats) – Shape of the distribution. Must be non-negative.
size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g.,
(m, n, k)
, thenm * n * k
samples are drawn. If size isNone
(default), a single value is returned ifa
is a scalar. Otherwise,np.array(a).size
samples are drawn.
- Returns
out – Drawn samples from the 1-parameter Weibull distribution.
- Return type
ndarray or scalar
Examples
>>> np.random.weibull(a=5) array(0.9553641) >>> np.random.weibull(a=5, size=[2,3]) array([[1.0466299 , 1.1320982 , 0.98415005], [1.1430776 , 0.9532727 , 1.1344457 ]]) >>> np.random.weibull(a=np.array([2,3]) array([0.98843634, 1.0125613 ]) The Weibull distribution is one of a class of Generalized Extreme Value (GEV) distributions. This class includes the Gumbel and Frechet distributions. The probability density for the Weibull distribution is f(x) = \frac{a}{\lambda}(\frac{x}{\lambda})^{a-1}e^{-(x/\lambda)^a}, where a is the shape and \lambda the scale. The generated 1-parameter Weibull sample has the scale parameter \lambda = 1. The Weibull distribution is commonly used in reliability engineering to model time to failure, in modeling particle sizes, in information retrieval to model dwell time on pages, in quantitative finance to model risk etc.
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